AIMC Topic: Normal Distribution

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A Hierarchical Convolutional Neural Network for vesicle fusion event classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neu...

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.

Computational and mathematical methods in medicine
Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue prop...

Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes.

BMC bioinformatics
BACKGROUND: Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain...

Phantom Validation of Tc-99m Absolute Quantification in a SPECT/CT Commercial Device.

Computational and mathematical methods in medicine
. Similar to PET, absolute quantitative imaging is becoming available in commercial SPECT/CT devices. This study's goal was to assess quantitative accuracy of activity recovery as a function of image reconstruction parameters and count statistics in ...

Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier.

Computational and mathematical methods in medicine
A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the ...

Complete stability of delayed recurrent neural networks with Gaussian activation functions.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditi...

Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

Medical engineering & physics
Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during ...

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate classification of different anatomical structures of teeth from medical images provides crucial information for the stress analysis in dentistry. Usually, the anatomical structures of teeth are manually labeled by experienced clinical doctor...

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

Neural networks : the official journal of the International Neural Network Society
The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learnin...

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition.

IEEE transactions on pattern analysis and machine intelligence
This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation...